This study analyzes long-term wind speed data from mountainous areas, focusing on the non-stationary characteristics of the wind field and their impact on wind speed simulations. Due to the complexity of wind speeds in mountainous regions, the paper proposes a correction model combining time-varying mean wind speed and nonstationary wind speed models. Through systematic preprocessing and stationarity testing, significant non-stationary features are identified, particularly in high wind speed ranges during strong gusts and complex terrain conditions. The time-varying mean wind speed and fluctuating wind speed show distinct evolving patterns, impacting simulation accuracy. To capture these characteristics, the Priestley Evolutionary Power Spectral Density (EPSD) method is employed to fit the time-varying power spectrum, creating a time-frequency model for non-stationary wind fields in mountainous areas. Compared to traditional stationary model, the proposed correction model provides better accuracy in handling multi-peak structures, low-frequency variations, and turbulence. A clustering analysis of multiple measured correction models is used to derive a representative model, offering more reliable parameters for wind field simulations and improving simulation accuracy. This research provides new methods and data support for simulating non-stationary wind fields.
In order to reduce the winter wind damage of solar greenhouses, a windbreak structure is designed, and its parameters (length and height) are comparatively evaluated using computational fluid dynamics (CFD) under different incoming air velocity (4 m center dot s-1 or 13.9 m center dot s-1) and different ventilation conditions (vents are fully closed or opened). The results shows that the windbreak structure with a height of 0.5 m and a length of 10 m can more effectively reduce the wind load shape coefficient compared to other cases. At two incoming air velocities (4 m center dot s-1 and 13.9 m center dot s-1), the wind load shape coefficient near the west gable (the LP1 and UP1 areas) is reduced by 23.57% and 24.88% compared with the non-windbreak structure when the greenhouse is closed, and by 12.12% and 15.97% when the greenhouse is opened. The average WRM (wind resistance efficiency per unit material) value of this windbreak structure specification is 0.61, and compared to other cases, resource utilization is more efficient under different conditions. In this study, the maximum deformation deflection of the windbreak structure under different conditions was 0.75 mm, meeting the requirements and demonstrating its practicality.
Skew wind and tower interference have significant impacts on the buffeting performance of cable-stayed bridges in the cantilever state. Based on a cable-stayed bridge, the effects of skew wind and tower aerodynamic interference on the aerodynamic admittance function (AAF) and buffeting response of the bridge at the maximum cantilever state are investigated. The results indicate that the AAFs are significantly smaller than the Sears function, particularly in the low-frequency region that contributes most to structural buffeting behavior. Skew wind and tower interference notably influence the bridge AAF. As the wind yaw angle increases, AAF generally exhibit a monotonically decreasing trend, indicating that the orthogonal wind direction being the most critical condition. For the main girder located leeward side of the tower, a larger wind yaw angle leads to more pronounced characteristic turbulence effects from the tower and greater local abrupt changes in AAF. Based on test results, empirical models of AAF under different wind yaw angles, accounting for skew wind and tower interference effect, are proposed. Under skew wind conditions with a same yaw angle, the presence of tower interference increases the bridge buffeting response. Furthermore, the tower interference effect is related to the distance between the girder segment and the tower, diminishing as the distance increases. Therefore, for the most critical cantilever state of cable-stayed bridges, the identification of AAF must realistically consider the effects of skew wind and tower interference under various conditions; otherwise, the evaluation of structural buffeting performance may be significantly inaccurate.
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This study investigates the vibration characteristics of 10 MW, 8 MW, and 6.7 MW large-capacity jacket-supported offshore wind turbines (OWTs) with varying conditions and foundation types in a deep-sea wind farm. By using an improved Natural Excitation Technique combined with Eigensystem Realization Algorithm (NExT-ERA) to accurately identify modal parameters of OWTs under operational harmonic interference across six operational phases (shutdown, startup, grid-connection, transition, rated speed, cut-out) using high-precision accelerometers (50 Hz) and SCADA data. Research on vibration characteristics revealed that the improved NExT-ERA algorithm, reduced harmonic-induced errors in fundamental frequencies by 0.2-0.5 Hz during operational states. Still, due to the complex operation of the grid-connection phase and the transition phase, the modal recognition showed a positive deviation and has a large degree of discreteness. Besides, the influence of foundation type on vibration amplitude is more significant near the foundation, while the effects of installed capacity and turbine manufacturer dominate closer to the tower. Advanced aerodynamic designs and modern control optimizations significantly reduce vibration amplitudes, thereby enhancing structural durability, as demonstrated by the greater vibration amplitudes of the 6.7 MW turbine compared to larger, more optimized turbines. While a single sensor can identify overall modes, the tower top's sensitivity to complex loading necessitates multi-level monitoring to comprehensively capture vibration modes, ensuring robust structural health assessment.
